Document Clustering & similarity value prediction with Python

Document duplicacy and similarity is an emerging issue which is the most efficient way to use storage efficiently. We can ignore duplicate documents and can increase our prosessing speed in a reasonable way.

Here in this example we have taken resume samples to identify which are simliar documents are their respective similarity ratio.

Here we have performed following steps to our resume documents which are very large set of data:

  1. tokenizing and stemming each synopsis
  2. transforming the corpus into vector space using tf-idf
  3. calculating cosine distance between each document as a measure of similarity
  4. clustering the documents using the k-means algorithm
  5. using multidimensional scaling to reduce dimensionality within the corpus
  6. plotting the clustering output using matplotlib and mpld3
  7. conducting a hierarchical clustering on the corpus using Ward clustering
  8. plotting a Ward dendrogram
  9. topic modeling using Latent Dirichlet Allocation (LDA)
  10. similarity matrix with respect to a bunch of documents.

Essential libraries listings

In [2]:
import numpy as np
import pandas as pd
import nltk
import re
import os
import codecs
from sklearn import feature_extraction
import mpld3

Path setting

In [211]:
import os
listdir = []
path = 'D:/xray production/resume backup/Docs_Backup/Resume_set_01/train'

List all Files

In [212]:
listdir = [os.path.join(dirpath, f)
for dirpath, dirnames, files in os.walk(path)
           for f in files if f.endswith('.txt')]

Function to tokenize and stemming of raw data

In [182]:
# here I define a tokenizer and stemmer which returns the set of stems in the text that it is passed

def tokenize_and_stem(text):
    # first tokenize by sentence, then by word to ensure that punctuation is caught as it's own token
    tokens = [word for sent in nltk.sent_tokenize(text) for word in nltk.word_tokenize(sent)]
    filtered_tokens = []
    # filter out any tokens not containing letters (e.g., numeric tokens, raw punctuation)
    for token in tokens:
        if re.search('[a-zA-Z]', token):
            filtered_tokens.append(token)
    stems = [stemmer.stem(t) for t in filtered_tokens]
    return stems


def tokenize_only(text):
    # first tokenize by sentence, then by word to ensure that punctuation is caught as it's own token
    tokens = [word.lower() for sent in nltk.sent_tokenize(text) for word in nltk.word_tokenize(sent)]
    filtered_tokens = []
    # filter out any tokens not containing letters (e.g., numeric tokens, raw punctuation)
    for token in tokens:
        if re.search('[a-zA-Z]', token):
            filtered_tokens.append(token)
    return filtered_tokens

NLTK reated libraries and stopwords updation

In [183]:
import logging
import os
import glob2
from nltk.corpus import stopwords
from nltk.tokenize import wordpunct_tokenize
import nltk
import re
import gensim
stop_words = set(stopwords.words('english'))
stop_words.update(['.', ',', '"', "'", '?', '!', ':', ';', '(', ')',
                   '[', ']', '{', '}','%','&','+','$','@','~','-','_','://','--','[]']) # remove it if you need punctuation 
from nltk.tokenize import RegexpTokenizer
from datetime import datetime
In [184]:
# load nltk's SnowballStemmer as variabled 'stemmer'
from nltk.stem.snowball import SnowballStemmer
stemmer = SnowballStemmer("english")
In [185]:
#not super pythonic, no, not at all.
#use extend so it's a big flat list of vocab
synopses = []
totalvocab_stemmed = []
totalvocab_tokenized = []
for fle in listdir:
   # open the file and then call .read() to get the text 
   with open(fle, encoding="latin-1") as f:
        text = f.read()
        s = text
        synopses.append(s)
In [186]:
 for i in synopses:
            allwords_stemmed = tokenize_and_stem(i) #for each item in 'synopses', tokenize/stem
            totalvocab_stemmed.extend(allwords_stemmed) #extend the 'totalvocab_stemmed' list

            allwords_tokenized = tokenize_only(i)
            totalvocab_tokenized.extend(allwords_tokenized)

Vocabulary data frame creation

In [187]:
vocab_frame = pd.DataFrame({'words': totalvocab_tokenized}, index = totalvocab_stemmed)
print('there are ' + str(vocab_frame.shape[0]) + ' items in vocab_frame')
there are 248824 items in vocab_frame

vocabs

In [188]:
print(vocab_frame.head())
                   words
applic         applicant
categori        category
profession  professional
requisit     requisition
code                code

TF-IDF MATRIX & VECTOR CREATION

In [189]:
from sklearn.feature_extraction.text import TfidfVectorizer

#define vectorizer parameters
tfidf_vectorizer = TfidfVectorizer(max_df=100000, max_features=700000,
                                 min_df=0, stop_words='english',
                                 use_idf=True, tokenizer=tokenize_and_stem, ngram_range=(1,3))

%time tfidf_matrix = tfidf_vectorizer.fit_transform(synopses) #fit the vectorizer to synopses

print(tfidf_matrix.shape)
Wall time: 9.56 s
(318, 259405)
In [190]:
terms = tfidf_vectorizer.get_feature_names()

Cosine similarity matrix

In [191]:
from sklearn.metrics.pairwise import cosine_similarity
dist = 1 - cosine_similarity(tfidf_matrix)

k-means clustering algorithm

In [192]:
from sklearn.cluster import KMeans

num_clusters = 5

km = KMeans(n_clusters=num_clusters)

%time km.fit(tfidf_matrix)

clusters = km.labels_.tolist()
Wall time: 15.3 s
In [193]:
from sklearn.externals import joblib

#uncomment the below to save your model 
#since I've already run my model I am loading from the pickle

joblib.dump(km,  'doc_cluster.pkl')

km = joblib.load('doc_cluster.pkl')
clusters = km.labels_.tolist()
In [194]:
films = { 'title': listdir, 'synopsis': synopses, 'cluster': clusters }

frame = pd.DataFrame(films, index = [clusters] , columns = ['title', 'cluster', 'synopsis'])

Cluster and entitled values in each cluster

In [195]:
frame['cluster'].value_counts() 
Out[195]:
3    79
2    66
4    61
1    57
0    55
Name: cluster, dtype: int64

Cluster and corresponding words and titles

In [29]:
from __future__ import print_function

print("Top terms per cluster:")
print()
#sort cluster centers by proximity to centroid
order_centroids = km.cluster_centers_.argsort()[:, ::-1] 

for i in range(num_clusters):
    print("Cluster %d words:" % i, end='')
    
    for ind in order_centroids[i, :6]: #replace 6 with n words per cluster
        print(' %s' % vocab_frame.ix[terms[ind].split(' ')].values.tolist()[0][0].encode('utf-8', 'ignore'), end=',')
    print() #add whitespace
    print() #add whitespace
    
    print("Cluster %d titles:" % i, end='')
    for title in frame.ix[i]['title'].values.tolist():
        print(' %s,' % title, end='')
    print() #add whitespace
    print() #add whitespace
    
print()
print()
Top terms per cluster:

Cluster 0 words: b'test', b'project', b'development', b'reports', b'oracle', b'used',

Cluster 0 titles: D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192752.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192820.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192822.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192830.txt,
C:\Users\amishra\AppData\Local\Continuum\Anaconda3\lib\site-packages\ipykernel_launcher.py:12: DeprecationWarning: 
.ix is deprecated. Please use
.loc for label based indexing or
.iloc for positional indexing

See the documentation here:
http://pandas.pydata.org/pandas-docs/stable/indexing.html#deprecate_ix
  if sys.path[0] == '':
C:\Users\amishra\AppData\Local\Continuum\Anaconda3\lib\site-packages\ipykernel_launcher.py:17: DeprecationWarning: 
.ix is deprecated. Please use
.loc for label based indexing or
.iloc for positional indexing

See the documentation here:
http://pandas.pydata.org/pandas-docs/stable/indexing.html#deprecate_ix
 D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192835.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192837.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192842.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192846.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192848.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192850.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192851.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192852.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192854.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192855.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192886.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192888.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192892.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192893.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192906.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192907.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192912.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192916.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192918.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192929.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192937.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192939.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192940.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192941.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192944.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192945.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192954.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192959.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192975.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192976.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192978.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192980.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192983.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192988.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192993.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192997.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192998.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\193003.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\193008.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\193014.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\193018.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\193024.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\193025.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\193027.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\193045.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\193054.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\193057.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\193058.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\193063.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\193066.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\193073.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\193074.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\193077.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\193078.txt,

Cluster 1 words: b'military', b'army', b'recruitmilitary', b'school', b'working', b'document',

Cluster 1 titles: D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192765.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192766.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192767.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192768.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192769.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192770.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192771.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192772.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192773.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192774.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192775.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192776.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192777.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192778.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192779.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192780.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192781.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192782.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192783.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192784.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192785.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192786.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192787.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192788.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192789.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192790.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192791.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192792.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192793.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192794.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192795.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192796.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192797.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192798.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192799.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192800.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192801.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192802.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192803.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192804.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192805.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192806.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192807.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192808.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192809.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192810.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192811.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192812.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192813.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192814.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192815.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192817.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192818.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192819.txt,

Cluster 2 words: b'network', b'servers', b'configuration', b'installation', b'hardware', b'manager',

Cluster 2 titles: D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192751.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192753.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192816.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192825.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192832.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192833.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192839.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192841.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192844.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192853.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192856.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192859.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192860.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192862.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192864.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192867.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192868.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192869.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192872.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192874.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192875.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192877.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192881.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192885.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192890.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192891.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192895.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192898.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192899.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192902.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192903.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192910.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192913.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192919.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192930.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192943.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192977.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\193006.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\193034.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\193035.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\193047.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\193048.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\193050.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\193056.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\193059.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\193069.txt,

Cluster 3 words: b'manager', b'sales', b'marketing', b'working', b'hr', b'productivity',

Cluster 3 titles: D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192821.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192823.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192824.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192826.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192828.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192829.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192831.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192834.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192836.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192838.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192840.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192843.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192845.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192847.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192849.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192857.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192858.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192861.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192863.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192866.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192870.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192873.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192876.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192878.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192879.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192880.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192882.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192883.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192884.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192887.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192889.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192894.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192896.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192900.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192901.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192904.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192905.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192908.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192909.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192911.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192920.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192921.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192924.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192928.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192936.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192950.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192952.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192967.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192973.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192990.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192999.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\193000.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\193007.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\193013.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\193015.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\193016.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\193020.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\193022.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\193028.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\193029.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\193030.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\193031.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\193032.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\193033.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\193036.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\193038.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\193039.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\193042.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\193043.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\193044.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\193046.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\193049.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\193051.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\193052.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\193053.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\193060.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\193061.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\193062.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\193064.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\193065.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\193067.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\193068.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\193070.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\193071.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\193072.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\193075.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\193079.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\193080.txt,

Cluster 4 words: b'specified', b'c', b'resumes', b'computer', b'skills', b'date',

Cluster 4 titles: D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192827.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192865.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192871.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192897.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192914.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192915.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192917.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192922.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192923.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192925.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192926.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192927.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192931.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192932.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192933.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192934.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192935.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192938.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192942.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192946.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192947.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192948.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192949.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192951.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192953.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192956.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192957.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192958.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192960.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192961.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192962.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192963.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192964.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192965.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192966.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192968.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192969.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192970.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192971.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192972.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192974.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192979.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192981.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192982.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192984.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192985.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192986.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192987.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192989.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192991.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192992.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192994.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192995.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\192996.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\193001.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\193002.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\193004.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\193005.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\193009.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\193010.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\193011.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\193012.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\193017.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\193019.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\193021.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\193023.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\193026.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\193037.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\193040.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\193041.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\193055.txt, D:/xray production/resume backup/Docs_Backup/Resume_set_01/train\193076.txt,



multidimentional scaling

In [30]:
import os  # for os.path.basename

import matplotlib.pyplot as plt
import matplotlib as mpl

from sklearn.manifold import MDS

MDS()

# convert two components as we're plotting points in a two-dimensional plane
# "precomputed" because we provide a distance matrix
# we will also specify `random_state` so the plot is reproducible.
mds = MDS(n_components=2, dissimilarity="precomputed", random_state=1)

pos = mds.fit_transform(dist)  # shape (n_components, n_samples)

xs, ys = pos[:, 0], pos[:, 1]
print()
print()

Giving cluster different colors and names

In [31]:
cluster_colors = {0: '#1b9e77', 1: '#d95f02', 2: '#7570b3', 3: '#e7298a', 4: '#66a61e'}
In [36]:
#set up cluster names using a dict
cluster_names = {0: 'first cluster', 
                 1: 'second cluster', 
                 2: 'third cluster', 
                 3: 'fourth cluster', 
                 4: 'fifth cluster'}

clusters plot using matplotlib

In [37]:
#some ipython magic to show the matplotlib plots inline
%matplotlib inline 

#create data frame that has the result of the MDS plus the cluster numbers and titles
df = pd.DataFrame(dict(x=xs, y=ys, label=clusters, title=listdir)) 

#group by cluster
groups = df.groupby('label')


# set up plot
fig, ax = plt.subplots(figsize=(17, 9)) # set size
ax.margins(0.05) # Optional, just adds 5% padding to the autoscaling

#iterate through groups to layer the plot
#note that I use the cluster_name and cluster_color dicts with the 'name' lookup to return the appropriate color/label
for name, group in groups:
    ax.plot(group.x, group.y, marker='o', linestyle='', ms=12, 
            label=cluster_names[name], color=cluster_colors[name], 
            mec='none')
    ax.set_aspect('auto')
    ax.tick_params(\
        axis= 'x',          # changes apply to the x-axis
        which='both',      # both major and minor ticks are affected
        bottom='off',      # ticks along the bottom edge are off
        top='off',         # ticks along the top edge are off
        labelbottom='off')
    ax.tick_params(\
        axis= 'y',         # changes apply to the y-axis
        which='both',      # both major and minor ticks are affected
        left='off',      # ticks along the bottom edge are off
        top='off',         # ticks along the top edge are off
        labelleft='off')
    
ax.legend(numpoints=1)  #show legend with only 1 point

#add label in x,y position with the label as the film title
for i in range(len(df)):
    ax.text(df.ix[i]['x'], df.ix[i]['y'], df.ix[i]['title'], size=8)  

    
    
plt.show() #show the plot

#uncomment the below to save the plot if need be
plt.savefig('clusters_small_noaxes.png', dpi=200)
C:\Users\amishra\AppData\Local\Continuum\Anaconda3\lib\site-packages\ipykernel_launcher.py:37: DeprecationWarning: 
.ix is deprecated. Please use
.loc for label based indexing or
.iloc for positional indexing

See the documentation here:
http://pandas.pydata.org/pandas-docs/stable/indexing.html#deprecate_ix
In [38]:
plt.close()

Scatter plot of clusters with points showing references to the corresponding file names

In [39]:
#define custom toolbar location
class TopToolbar(mpld3.plugins.PluginBase):
    """Plugin for moving toolbar to top of figure"""

    JAVASCRIPT = """
    mpld3.register_plugin("toptoolbar", TopToolbar);
    TopToolbar.prototype = Object.create(mpld3.Plugin.prototype);
    TopToolbar.prototype.constructor = TopToolbar;
    function TopToolbar(fig, props){
        mpld3.Plugin.call(this, fig, props);
    };

    TopToolbar.prototype.draw = function(){
      // the toolbar svg doesn't exist
      // yet, so first draw it
      this.fig.toolbar.draw();

      // then change the y position to be
      // at the top of the figure
      this.fig.toolbar.toolbar.attr("x", 150);
      this.fig.toolbar.toolbar.attr("y", 400);

      // then remove the draw function,
      // so that it is not called again
      this.fig.toolbar.draw = function() {}
    }
    """
    def __init__(self):
        self.dict_ = {"type": "toptoolbar"}
In [112]:
#create data frame that has the result of the MDS plus the cluster numbers and titles
df = pd.DataFrame(dict(x=xs, y=ys, label=clusters, title=listdir)) 

#group by cluster
groups = df.groupby('label')

#define custom css to format the font and to remove the axis labeling
css = """
text.mpld3-text, div.mpld3-tooltip {
  font-family:Arial, Helvetica, sans-serif;
}

g.mpld3-xaxis, g.mpld3-yaxis {
display: none; }

svg.mpld3-figure {
margin-left: -200px;}
"""

# Plot 
fig, ax = plt.subplots(figsize=(14,6)) #set plot size
ax.margins(0.03) # Optional, just adds 5% padding to the autoscaling

#iterate through groups to layer the plot
#note that I use the cluster_name and cluster_color dicts with the 'name' lookup to return the appropriate color/label
for name, group in groups:
    points = ax.plot(group.x, group.y, marker='o', linestyle='', ms=18, 
                     label=cluster_names[name], mec='none', 
                     color=cluster_colors[name])
    ax.set_aspect('auto')
    labels = [i for i in group.title]
    
    #set tooltip using points, labels and the already defined 'css'
    tooltip = mpld3.plugins.PointHTMLTooltip(points[0], labels,
                                       voffset=10, hoffset=10, css=css)
    #connect tooltip to fig
    mpld3.plugins.connect(fig, tooltip, TopToolbar())    
    
    #set tick marks as blank
    ax.axes.get_xaxis().set_ticks([])
    ax.axes.get_yaxis().set_ticks([])
    
    #set axis as blank
    ax.axes.get_xaxis().set_visible(False)
    ax.axes.get_yaxis().set_visible(False)

    
ax.legend(numpoints=1) #show legend with only one dot

mpld3.display() #show the plot

#uncomment the below to export to html
html = mpld3.fig_to_html(fig)
Out[112]:

Hierarchical document clustering

In [46]:
from scipy.cluster.hierarchy import ward, dendrogram

linkage_matrix = ward(dist) #define the linkage_matrix using ward clustering pre-computed distances

fig, ax = plt.subplots(figsize=(15, 20)) # set size
ax = dendrogram(linkage_matrix, orientation="right", labels=listdir);

plt.tick_params(\
    axis= 'x',          # changes apply to the x-axis
    which='both',      # both major and minor ticks are affected
    bottom='off',      # ticks along the bottom edge are off
    top='off',         # ticks along the top edge are off
    labelbottom='off')

plt.tight_layout() #show plot with tight layout

#uncomment below to save figure
plt.savefig('ward_clusters.png', dpi=200) #save figure as ward_clusters
In [47]:
plt.close()

Latent Dirichlet Allocation

In [48]:
#strip any proper names from a text...unfortunately right now this is yanking the first word from a sentence too.
import string
def strip_proppers(text):
    # first tokenize by sentence, then by word to ensure that punctuation is caught as it's own token
    tokens = [word for sent in nltk.sent_tokenize(text) for word in nltk.word_tokenize(sent) if word.islower()]
    return "".join([" "+i if not i.startswith("'") and i not in string.punctuation else i for i in tokens]).strip()
In [49]:
#strip any proper nouns (NNP) or plural proper nouns (NNPS) from a text
from nltk.tag import pos_tag

def strip_proppers_POS(text):
    tagged = pos_tag(text.split()) #use NLTK's part of speech tagger
    non_propernouns = [word for word,pos in tagged if pos != 'NNP' and pos != 'NNPS']
    return non_propernouns
In [51]:
from gensim import corpora, models, similarities 

#remove proper names
%time preprocess = [strip_proppers(doc) for doc in synopses]

#tokenize
%time tokenized_text = [tokenize_and_stem(text) for text in preprocess]
Wall time: 3.58 s
Wall time: 2.72 s
In [68]:
stop_words = set(stopwords.words('english'))
stop_words.update(['.', ',', '"', "'", '?', '!', ':', ';', '(', ')',
                   '[', ']', '{', '}','%','&','+','$','@','~','-','_','://','--','[]']) # remove it if you need punctuation 
In [69]:
#remove stop words
%time texts = [[word for word in text if word not in stop_words] for text in tokenized_text]
Wall time: 25 ms
In [70]:
#create a Gensim dictionary from the texts
dictionary = corpora.Dictionary(texts)

#remove extremes (similar to the min/max df step used when creating the tf-idf matrix)
dictionary.filter_extremes(no_below=1, no_above=0.8)

#convert the dictionary to a bag of words corpus for reference
corpus = [dictionary.doc2bow(text) for text in texts]
In [74]:
%time lda = models.LdaModel(corpus, num_topics=5, id2word=dictionary, update_every=5, chunksize=10000, passes=100)
Wall time: 4min 32s
In [76]:
lda.show_topics()
Out[76]:
[(0,
  '0.014*"system" + 0.013*"month" + 0.013*"use" + 0.011*"specifi" + 0.010*"gmail.com" + 0.009*"test" + 0.009*"data" + 0.009*"work" + 0.009*"applic" + 0.009*"manag"'),
 (1,
  '0.021*"work" + 0.018*"specifi" + 0.011*"gmail.com" + 0.011*"project" + 0.009*"engin" + 0.009*"materi" + 0.007*"draw" + 0.006*"system" + 0.006*"per" + 0.006*"site"'),
 (2,
  '0.012*"experi" + 0.010*"militari" + 0.010*"equip" + 0.009*"level" + 0.009*"oper" + 0.008*"like" + 0.008*"search" + 0.008*"help" + 0.007*"question" + 0.007*"https"'),
 (3,
  '0.011*"work" + 0.006*"financi" + 0.005*"project" + 0.005*"account" + 0.005*"skill" + 0.005*"gmail.com" + 0.005*"perform" + 0.004*"train" + 0.004*"oper" + 0.004*"specifi"'),
 (4,
  '0.014*"manag" + 0.012*"team" + 0.011*"train" + 0.011*"product" + 0.010*"custom" + 0.009*"sale" + 0.009*"busi" + 0.008*"process" + 0.008*"month" + 0.008*"work"')]
In [111]:
topics_matrix = lda.show_topics(formatted=False, num_words=20)
topics_matrix = np.array(topics_matrix,dtype=object)

topic_words = topics_matrix[:,1]
for i in topic_words:
    print([str(word) for word in i])
    print()
["('system', 0.013930067263968811)", "('month', 0.012975108381266243)", "('use', 0.012696189416809028)", "('specifi', 0.010734408675545164)", "('gmail.com', 0.0097738064518517551)", "('test', 0.0094430471895764635)", "('data', 0.0090699297840079497)", "('work', 0.0090291334880692464)", "('applic', 0.0087123249810280908)", "('manag', 0.0086790843163894668)", "('develop', 0.0085864569826078919)", "('support', 0.0084026275193507475)", "('project', 0.0083391632664919727)", "('network', 0.0081552516574210122)", '("\'s", 0.0074209337151694265)', "('server', 0.0070817186518829399)", "('user', 0.0069362805436867098)", "('team', 0.0061214123002106)", "('experi', 0.005985466848800935)", "('softwar', 0.0059212352661100328)"]

["('work', 0.020749515343922807)", "('specifi', 0.017795487007731838)", "('gmail.com', 0.01117747363893045)", "('project', 0.01052765394396204)", "('engin', 0.0091739740322179589)", "('materi', 0.0091465900454982627)", "('draw', 0.0074857332410223501)", "('system', 0.0060185492399684685)", "('per', 0.0058305082174600811)", "('site', 0.0056800336689002608)", "('report', 0.0054445895847909489)", "('u', 0.0050804782914841036)", "('respons', 0.0050381095440077215)", "('compani', 0.0048183000306104701)", "('procur', 0.0047220632092786468)", "('knowledg', 0.0045983400363514759)", "('offic', 0.0045441442899460185)", "('relat', 0.0044424991312514801)", "('organ', 0.0043137244542382361)", "('depart', 0.0042029447374624463)"]

["('experi', 0.012184798102428252)", "('militari', 0.010252787998611851)", "('equip', 0.0099335999976050953)", "('level', 0.0091669711248801838)", "('oper', 0.008950118965318268)", "('like', 0.0084846817988243634)", "('search', 0.0076955837567458437)", "('help', 0.0075414675779583665)", "('question', 0.0072223045519164376)", "('https', 0.0071684739750607837)", "('sign', 0.0071008138854451097)", "('se', 0.0070749404554425336)", "('candidateâ\\x80\\x99', 0.007074940442536524)", "('jowal', 0.0070749403001418741)", "('microsoft.com', 0.0070749402013923109)", "('able_to_start_in', 0.007074940200935396)", "('demo', 0.0070686067959676458)", "('educ', 0.0070575282693635241)", "('gmail.com', 0.0066903236114375349)", "('mainten', 0.0057050791904132859)"]

["('work', 0.010913718156158211)", "('financi', 0.0063066699386220351)", "('project', 0.0053101691596525178)", "('account', 0.0050999956066103223)", "('skill', 0.0050839839293918901)", "('gmail.com', 0.0048756259134724155)", "('perform', 0.004810146806259275)", "('train', 0.0044301904639987852)", "('oper', 0.0043973058220973535)", "('specifi', 0.0042313940588412959)", "('compani', 0.0042212832313983538)", "('per', 0.00420388827428633)", "('month', 0.0040447253335422137)", "('inventori', 0.0039844324167890004)", "('posit', 0.0039182962953043616)", "('client', 0.0038530551719803039)", "('day', 0.0035440357180713208)", "('cost', 0.0035437238033319786)", "('custom', 0.0034447838248956347)", "('would', 0.0034219684148195789)"]

["('manag', 0.013798482043245967)", "('team', 0.012082690697034272)", "('train', 0.01142888828557498)", "('product', 0.010616626347206821)", "('custom', 0.010219317425860231)", "('sale', 0.0093850973329967832)", "('busi', 0.009285692189428648)", "('process', 0.0081632891446117781)", "('month', 0.0077890355054027185)", "('work', 0.0075896736800478773)", "('client', 0.0073507448223083362)", "('develop', 0.006825933413822087)", "('market', 0.006633452119516518)", "('plan', 0.0063189307566972382)", "('new', 0.0063164800793325442)", "('report', 0.0061931891248566137)", "('various', 0.0059690050189707402)", "('employe', 0.0058563060582631455)", "('ensur', 0.0057431148931711079)", "('servic', 0.0057308517220293993)"]

test data prediction

In [202]:
import os
listdir = []
path = 'D:/xray production/resume backup/Docs_Backup/Resume_set_01/train'
listdir = [os.path.join(dirpath, f)
for dirpath, dirnames, files in os.walk(path)
           for f in files if f.endswith('.txt')]
#not super pythonic, no, not at all.
#use extend so it's a big flat list of vocab
synopses = []
totalvocab_stemmed = []
totalvocab_tokenized = []
for fle in listdir:
   # open the file and then call .read() to get the text 
   with open(fle, encoding="latin-1") as f:
        text = f.read()
        s = text
        synopses.append(s)
        
for i in synopses:
            allwords_stemmed = tokenize_and_stem(i) #for each item in 'synopses', tokenize/stem
            totalvocab_stemmed.extend(allwords_stemmed) #extend the 'totalvocab_stemmed' list

            allwords_tokenized = tokenize_only(i)
            totalvocab_tokenized.extend(allwords_tokenized)
            
vocab_frame = pd.DataFrame({'words': totalvocab_tokenized}, index = totalvocab_stemmed)
print('there are ' + str(vocab_frame.shape[0]) + ' items in vocab_frame')

from sklearn.feature_extraction.text import TfidfVectorizer

#define vectorizer parameters
tfidf_vectorizer = TfidfVectorizer(max_df=100000, max_features=259405,
                                 min_df=0, stop_words='english',
                                 use_idf=True, tokenizer=tokenize_and_stem, ngram_range=(1,3))

%time tfidf_matrix = tfidf_vectorizer.fit_transform(synopses) #fit the vectorizer to synopses

print(tfidf_matrix.shape)
there are 251352 items in vocab_frame
Wall time: 11.6 s
(335, 259405)
In [203]:
tfidf_matrix
Out[203]:
<335x259405 sparse matrix of type '<class 'numpy.float64'>'
	with 435010 stored elements in Compressed Sparse Row format>
In [204]:
km.predict(tfidf_matrix)
Out[204]:
array([3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,
       3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,
       3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,
       3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,
       3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,
       3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,
       3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,
       3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,
       3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,
       3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,
       3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,
       3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,
       3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,
       3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,
       3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3])

Similarity matrix creation

In [213]:
# iterate over the list getting each file
vocab = []
dictionary = []
dic_tf = {}
dic_df = {}
for fle in listdir:
   # open the file and then call .read() to get the text 
   with open(fle, encoding="latin-1") as f:
        text = f.read()
        s = text
        text = ' '.join(w for w in re.split(r"\W", s) if w)  
        text = wordpunct_tokenize(text)
        for t in text:
            if t not in stop_words: 
                if not t.isdigit():
                    t=t.lower()
            if t.isdigit():
                text.remove(t)
        dictionary.append((text))

Dicitonary creation

In [214]:
import gensim
dictio = gensim.corpora.Dictionary(dictionary)
In [215]:
corpus = [dictio.doc2bow(gen_doc) for gen_doc in dictionary]
In [216]:
tf_idf = gensim.models.TfidfModel(corpus)
In [219]:
sims = gensim.similarities.Similarity('D:/xray production/resume backup/Docs_Backup',tf_idf[corpus],
                                      num_features=len(dictio))
In [220]:
print(sims)
print(type(sims))
Similarity index with 318 documents in 0 shards (stored under D:/xray production/resume backup/Docs_Backup)
<class 'gensim.similarities.docsim.Similarity'>

Test the similarity model

In [223]:
# iterate over the list getting each file
text = []
listdir = []
path = 'D:/xray production/resume backup/Docs_Backup/Resume_set_01/test'

listdir = [os.path.join(dirpath, f)
    for dirpath, dirnames, files in os.walk(path)
    for f in files if f.endswith('.txt')]
for fle in listdir:
   # open the file and then call .read() to get the text 
   with open(fle, encoding="latin-1") as f:
         text.append(f.read())
query_doc = []
from nltk.tokenize import RegexpTokenizer
for i in range(len(text)):
        s = text[i]
        text[i] = ' '.join(w for w in re.split(r"\W", s) if w)
for t in text:
    new_data = [i.lower() for i in wordpunct_tokenize(t) if i.lower() not in stop_words if not i.isdigit()]
    
    for n in new_data:
        if n.isdigit():
                new_data.remove(n)
        
    query_doc.append(new_data)

print(query_doc)
[['applicant', 'category', 'professional', 'requisition', 'code', 'two', 'initials', 'recruiter', 'lb', 'system', 'date', 'system', 'time', 'document', 'name', 'william', 'ward', 'ashmore', 'source', 'clearancejobs', 'com', 'resumes', 'searchterms', 'software', 'development', 'coding', 'test', 'researcher', 'build', 'user', 'interface', 'user', 'experience', 'url', 'http', 'www', 'clearancejobs', 'com', 'resumes', 'william', 'ward', 'ashmore', 'document', 'date', 'document', 'time', 'document', 'id', '42d62n82t', 'email', 'address', 'william', 'w', 'ashmore', 'gmail', 'com', 'location', 'al', 'start', 'resume', 'text', 'william', 'ward', 'ashmore', 'old', 'farm', 'lane', 'apt', 'e', 'prattville', 'alabama', 'date', 'added', 'contact', 'info', 'email', 'william', 'w', 'ashmore', 'gmail', 'com', 'mobile', 'security', 'clearance', 'clearance', 'status', 'active', 'security', 'clearance', 'clearance', 'level', 'secret', 'clearance', 'expiration', 'september', 'issuing', 'agency', 'dept', 'defense', 'polygraph', 'none', 'military', 'service', 'primary', 'branch', 'none', 'status', 'job', 'category', 'hardware', 'software', 'prog', 'career', 'level', 'entry', 'level', 'less', 'yrs', 'experience', 'type', 'passively', 'considering', 'jobs', 'geography', 'willing', 'relocate', 'yes', 'ideal', 'locations', 'al', 'birmingham', 'al', 'montgomery', 'al', 'northern', 'huntsville', 'compensation', 'desired', 'annual', 'unspecified', 'desired', 'hourly', 'unspecified', 'resume', 'body', 'william', 'ashmore', 'william', 'w', 'ashmore', 'gmail', 'com', 'old', 'farm', 'lane', 'apt', 'e', 'prattville', 'al', 'education', 'mississippi', 'state', 'university', 'starkville', 'ms', 'business', 'information', 'systems', 'graduated', 'may', 'holmes', 'community', 'college', 'grenada', 'campus', 'grenada', 'ms', 'associate', 'implied', 'science', 'graduated', 'may', 'profile', 'self', 'motivated', 'individual', 'yearning', 'willing', 'learn', 'much', 'looking', 'expand', 'career', 'career', 'field', 'currently', 'working', 'mcsa', 'mcse', 'certifications', 'certifications', 'security', 'november', 'present', 'comptia', 'security', 'edition', 'certification', 'exam', 'secret', 'security', 'clearance', 'september', 'september', 'ashmore', 'william', 'w', 'knowledge', 'skills', 'â', 'comptia', 'security', 'â', 'windows', 'operating', 'systems', 'â', 'ms', 'office', 'word', 'excel', 'outlook', 'access', 'powerpoint', 'â', 'internet', 'explorer', 'mozilla', 'browsers', 'â', 'bmc', 'remedy', 'ticket', 'manager', 'system', 'â', 'installation', 'cat', 'fiber', 'cables', 'â', 'active', 'directory', 'administration', 'â', 'database', 'management', 'â', 'air', 'force', 'network', 'operations', 'â', 'troubleshooting', 'desktops', 'laptops', 'printers', 'scanners', 'pdas', 'projectors', 'â', 'digital', 'certificates', 'â', 'dreamweaver', 'suite', 'â', 'anti', 'virus', 'tools', 'â', 'knowledge', 'c', 'html', 'java', 'script', 'cobol', 'visual', 'basic', 'rpg', 'oracle', 'sql', 'â', 'remote', 'administration', 'remote', 'assistance', 'â', 'adding', 'workstations', 'domain', 'â', 'access', 'control', 'methods', 'â', 'encryption', 'standards', 'â', 'network', 'device', 'mapping', 'â', 'microsoft', 'exchange', 'servers', 'â', 'administrative', 'assistant', 'â', 'installation', 'tele', 'conference', 'systems', 'â', 'commissioned', 'decommissioned', 'servers', 'â', 'niprnet', 'siprnet', 'systems', 'experience', 'network', 'support', 'specialist', 'integrated', 'computer', 'solutions', 'inc', 'montgomery', 'al', 'september', 'present', 'disa', 'september', 'â', 'present', 'gunter', 'air', 'force', 'base', 'experience', 'duties', 'â', 'install', 'fiber', 'cat', 'cat', 'cables', 'throughout', 'building', 'â', 'help', 'ensure', 'server', 'roomâ', 'run', 'without', 'connectivity', 'loss', 'â', 'troubleshoot', 'various', 'connectivity', 'issues', 'users', 'building', 'â', 'install', 'troubleshoot', 'phone', 'systems', 'voip', 'systems', 'â', 'terminate', 'cat', 'fiber', 'cables', 'needed', 'â', 'coordinate', 'user', 'moves', 'throughout', 'building', 'ensure', 'network', 'loss', 'minimum', 'â', 'coordinate', 'central', 'communications', 'center', 'ccc', 'port', 'security', 'issues', 'positions', 'promotions', 'â', 'network', 'support', 'specialist', 'â', 'september', 'network', 'support', 'technician', 'indus', 'corp', 'montgomery', 'al', 'june', 'september', 'enterprise', 'service', 'desk', 'june', 'â', 'present', 'gunter', 'air', 'force', 'base', 'experience', 'duties', 'â', 'provide', 'telephone', 'technical', 'support', 'â', 'network', 'administration', 'remedy', 'administration', 'â', 'problem', 'resolution', 'advanced', 'troubleshoot', 'support', 'â', 'provide', 'remote', 'assistance', 'remote', 'administration', 'â', 'train', 'employees', 'steps', 'troubleshoot', 'issues', 'positions', 'promotions', 'â', 'level', 'helpdesk', 'network', 'support', 'technician', 'â', 'june', 'â', 'level', 'ii', 'helpdesk', 'network', 'support', 'technician', 'â', 'november', 'â', 'advanced', 'level', 'helpdesk', 'network', 'support', 'technician', 'â', 'september', 'â', 'promoted', 'remote', 'administration', 'pit', 'december', 'â', 'promoted', 'area', 'migration', 'pit', 'may', 'co', 'op', 'worker', 'american', 'cast', 'iron', 'pipe', 'company', 'acipco', 'birmingham', 'al', 'january', 'may', 'august', 'â', 'december', 'may', 'july', 'experience', 'duties', 'â', 'perform', 'maintenance', 'printers', 'computers', 'various', 'devices', 'â', 'updated', 'sql', 'programs', 'improve', 'productivity', 'â', 'updated', 'access', 'database', 'include', 'new', 'variables', 'â', 'perform', 'general', 'office', 'duties', 'positions', 'â', 'co', 'op', 'worker', 'student', 'technician', 'specialist', 'high', 'performance', 'computing', 'collaboratory', 'â', 'starkville', 'ms', 'august', 'â', 'december', 'may', 'â', 'july', 'january', 'â', 'may', 'august', 'â', 'may', 'experience', 'duties', 'â', 'perform', 'maintenance', 'printers', 'computers', 'various', 'devices', 'repairs', 'throughout', 'building', 'â', 'set', 'tele', 'conference', 'systems', 'â', 'install', 'cat', 'cat', 'fiber', 'cables', 'building', 'â', 'answer', 'user', 'inquiries', 'regarding', 'computer', 'software', 'positions', 'â', 'student', 'technician', 'specialist', 'â', 'august', 'additional', 'information', 'honors', 'activities', 'level', 'ii', 'employee', 'month', 'highest', 'amount', 'fixed', 'tickets', 'december', 'young', 'alumni', 'representative', 'mississippi', 'state', 'alumni', 'association', 'montgomery', 'area', 'resume', 'flag', 'add', 'network', 'end', 'resume', 'text'], ['applicant', 'category', 'professional', 'requisition', 'code', 'two', 'initials', 'recruiter', 'lb', 'system', 'date', 'system', 'time', 'document', 'name', 'jerry', 'bolanos', 'source', 'careerbuilder', 'resumes', 'wsi', 'recent', 'searchterms', 'software', 'development', 'coding', 'test', 'researcher', 'build', 'user', 'interface', 'user', 'experience', 'url', 'https', 'ws', 'careerbuilder', 'com', 'resumes', 'resumes', 'asmx', 'v2_getresume', 'packet', 'packet', 'document', 'date', 'document', 'time', 'document', 'id', '42i42n7yy', 'email', 'address', 'jerrybolanos', 'hotmail', 'com', 'location', 'ca', 'start', 'resume', 'text', 'jerry', 'bolanos', 'workman', 'mill', 'road', 'unit', 'whittier', 'ca', 'cell', 'email', 'jerrybolanos', 'hotmail', 'com', 'professional', 'experience', 'beamonstar', 'products', 'inc', 'mfg', 'novelty', 'toys', 'enhancements', 'queen', 'creek', 'az', 'may', 'august', 'operations', 'accounting', 'manager', 'responsible', 'financial', 'activities', 'performed', 'day', 'day', 'accounting', 'functions', 'manage', 'daily', 'cash', 'flow', 'maintain', 'general', 'ledger', 'system', 'banking', 'credit', 'card', 'transactions', 'online', 'website', 'payments', 'issuance', 'review', 'check', 'credit', 'card', 'expenditures', 'oversee', 'account', 'receivables', 'review', 'r', 'aging', 'summary', 'followed', 'customers', 'collections', 'overdue', 'payments', 'cost', 'accounting', 'standard', 'cost', 'accounting', 'developed', 'maintained', 'bill', 'materials', 'costing', 'finished', 'good', 'products', 'review', 'accuracy', 'costs', 'inventory', 'perform', 'inventory', 'valuation', 'variance', 'analysis', 'explanations', 'analysis', 'freight', 'shipping', 'costs', 'inventory', 'control', 'perform', 'monthly', 'physical', 'inventory', 'analysis', 'inventory', 'discrepancies', 'prepared', 'inventory', 'turnover', 'reports', 'inventory', 'aging', 'reports', 'identified', 'slow', 'moving', 'obsolete', 'items', 'min', 'max', 'levels', 'inventory', 'procurement', 'purchasing', 'sourcing', 'contract', 'manufacturers', 'private', 'labeling', 'within', 'pharmaceutical', 'industry', 'sourcing', 'printing', 'packaging', 'suppliers', 'fulfillment', 'centers', 'trucking', 'freight', 'carriers', 'along', 'purchasing', 'supplies', 'needed', 'temple', 'inland', 'inc', 'paperboard', 'box', 'manufacturing', 'bell', 'ca', 'may', 'april', 'accounting', 'office', 'manager', 'performed', 'month', 'end', 'activities', 'especially', 'related', 'inventory', 'supervision', 'weekly', 'payroll', 'processing', 'union', 'employees', 'supervise', 'accounts', 'receivables', 'collection', 'receivables', 'receipt', 'incoming', 'goods', 'services', 'via', 'purchase', 'orders', 'jd', 'edwards', 'system', 'cost', 'accounting', 'performed', 'inventory', 'valuation', 'variance', 'analysis', 'labor', 'utilization', 'reports', 'reviewed', 'inventory', 'costs', 'accuracy', 'created', 'maintained', 'updated', 'bill', 'materials', 'costing', 'products', 'set', 'procedures', 'adjust', 'accounting', 'policies', 'mandated', 'corporate', 'office', 'physical', 'inventory', 'overseen', 'weekly', 'cycle', 'counts', 'performed', 'monthly', 'physical', 'inventories', 'raw', 'material', 'roll', 'stock', 'finished', 'goods', 'variance', 'analysis', 'major', 'inventory', 'discrepancies', 'analyzed', 'variances', 'made', 'adjustments', 'inventory', 'needed', 'reporting', 'capital', 'expenditures', 'fixed', 'assets', 'key', 'performance', 'indicators', 'ran', 'customized', 'jd', 'edwards', 'reports', 'sales', 'department', 'coordinated', 'audits', 'necessary', 'l', 'shower', 'door', 'enclosures', 'manufacturing', 'ontario', 'ca', 'december', 'march', 'accounting', 'consultant', 'contractor', 'performed', 'month', 'end', 'closing', 'process', 'bank', 'account', 'reconciliations', 'prepared', 'year', 'end', 'audit', 'lead', 'schedules', 'supporting', 'schedules', 'labor', 'payroll', 'reporting', 'cost', 'accounting', 'prepared', 'variance', 'analysis', 'materials', 'usage', 'purchase', 'price', 'variances', 'assisted', 'expediting', 'monthly', 'reporting', 'procedures', 'accretive', 'solutions', 'accounting', 'finance', 'consulting', 'irvine', 'ca', 'july', 'october', 'business', 'consultant', 'contractor', 'unitedhealth', 'group', 'formerly', 'known', 'pacificare', 'cypress', 'ca', 'authentic', 'specialty', 'foods', 'la', 'victoria', 'embasa', 'food', 'manufacturing', 'chino', 'ca', 'october', 'may', 'director', 'production', 'planning', 'quality', 'systems', 'rosemead', 'plant', 'june', 'may', 'manager', 'cost', 'accounting', 'plant', 'controller', 'rosemead', 'plant', 'october', 'may', 'production', 'planning', 'preparation', 'annual', 'weekly', 'daily', 'production', 'plan', 'plant', 'six', 'production', 'lines', 'hispanic', 'food', 'products', 'salsas', 'sauces', 'fresh', 'produce', 'experience', 'understanding', 'material', 'availability', 'production', 'line', 'constraints', 'downtime', 'produce', 'seasonality', 'packaging', 'solid', 'understanding', 'various', 'types', 'packaging', 'glass', 'bottles', 'jars', 'plastic', 'jugs', 'bottles', 'metal', 'cans', 'flexible', 'cups', 'portion', 'control', 'pouches', 'pc', 'cost', 'accounting', 'sound', 'understanding', 'standard', 'cost', 'system', 'month', 'end', 'close', 'preparing', 'financial', 'statements', 'performing', 'variance', 'analysis', 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'hillsdale', 'avenue', 'san', 'jose', 'ca', 'us', 'mini_kapur', 'yahoo', 'com', 'mobile', 'home', 'phone', 'contact', 'preference', 'email', 'resume', 'monster', 'resume', 'bjd7qi9z725v6a5d', 'resume', 'headline', 'mini', 'kapur', 'objective', 'seeking', 'organization', 'learn', 'well', 'expand', 'knowledge', 'experience', 'interested', 'position', 'provide', 'compassionate', 'courteous', 'professional', 'services', 'customers', 'meticulously', 'alertness', 'experience', 'present', 'bascom', 'pharmacy', 'industry', 'retail', 'pharmacy', 'intern', 'pro', 'actively', 'build', 'expertise', 'complete', 'pharmaceutical', 'service', 'cycle', 'preparation', 'pharmaceuticals', 'drug', 'dispensation', 'customer', 'service', 'feedback', 'communication', 'attain', 'exposure', 'training', 'information', 'dissemination', 'client', 'patient', 'care', 'cash', 'control', 'management', 'well', 'supply', 'maintenance', 'management', 'preparing', 'prior', 'authorization', 'reports', 'receiving', 'approvals', 'specialty', 'drugs', 'ensure', 'prescriptions', 'fulfillment', 'maintaining', 'complete', 'customer', 'patient', 'confidentiality', 'interact', 'physicians', 'dentists', 'nurses', 'others', 'health', 'related', 'fields', 'engage', 'customer', 'service', 'improvements', 'facilitate', 'security', 'loss', 'prevention', 'procedures', 'ordering', 'practices', 'protocols', 'well', 'vendor', 'relations', 'inventory', 'merchandise', 'levels', 'management', 'retail', 'pharmacy', 'industry', 'retail', 'retail', 'pharmacy', 'attain', 'knowledge', 'inventory', 'management', 'cash', 'control', 'management', 'ordering', 'dispensing', 'medication', 'polytechnic', 'industry', 'government', 'military', 'lecturer', 'mentoring', 'students', 'conducting', 'laboratory', 'work', 'teaching', 'pharmacology', 'checking', 'grading', 'examination', 'papers', 'established', 'positive', 'relationship', 'fellow', 'lecturers', 'students', 'parents', 'administrative', 'staff', 'design', 'curriculum', 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'fluent', 'spanish', 'intermediate', 'summary', 'current', 'career', 'level', 'experienced', 'non', 'manager', 'years', 'relevant', 'work', 'experience', 'years', 'date', 'availability', 'months', 'work', 'status', 'us', 'authorized', 'work', 'country', 'employer', 'active', 'security', 'clearance', 'citizenship', 'target', 'job', 'target', 'job', 'title', 'mini', 'kapur', 'rph', 'ca', 'desired', 'job', 'type', 'employee', 'temporary', 'contract', 'project', 'seasonal', 'desired', 'status', 'full', 'time', 'part', 'time', 'target', 'company', 'occupation', 'biotech', 'r', 'science', 'clinical', 'research', 'pharmaceutical', 'research', 'medical', 'health', 'pharmacy', 'industry', 'biotechnology', 'pharmaceuticals', 'retail', 'healthcare', 'services', 'nonprofit', 'charitable', 'organizations', 'target', 'locations', 'selected', 'locations', 'us', 'ca', 'us', 'nv', 'us', 'tx', 'relocate', 'yes', 'willingness', 'travel', 'travel', 'basic', 'profile', 'resume', 'posted', 'target', 'employer', 'categories', 'biotech', 'r', 'science', 'medical', 'health', 'target', 'employer', 'occupations', 'clinical', 'research', 'target', 'job', 'titles', 'mini', 'kapur', 'rph', 'ca', 'candidate', 'name', 'mini', 'kapur', 'location', 'hillsdale', 'avenue', 'san', 'jose', 'ca', 'email', 'mini_kapur', 'yahoo', 'com', 'highest', 'degree', 'master', 'degree', 'post', 'graduate', 'msc', 'mcom', 'llb', 'recent', 'salary', 'employer', 'history', 'title', 'dates', 'bascom', 'pharmacy', 'pharmacy', 'intern', 'ohms', 'lab', 'intern', 'drug', 'analyst', 'polytechnic', 'lecturer', 'end', 'resume', 'text'], ['applicant', 'category', 'professional', 'requisition', 'code', 'two', 'initials', 'recruiter', 'lb', 'system', 'date', 'system', 'time', 'document', 'name', 'barton', 'miranda', 'source', 'monster', 'com', 'resumes', 'api', 'recent', 'searchterms', 'researcher', 'build', 'user', 'interface', 'user', 'experience', 'url', 'https', 'gateway', 'monster', 'com', 'bgwbroker', 'xml', 'version', 'encoding', 'utf', 'document', 'date', 'document', 'time', 'document', 'id', '86vc3qfm4', 'email', 'address', 'bartmiranda', 'gmail', 'com', 'location', 'va', 'start', 'resume', 'text', 'barton', 'miranda', 'sterling', 'va', 'us', 'bartmiranda', 'gmail', 'com', 'mobile', 'home', 'phone', 'contact', 'preference', 'email', 'resume', 'monster', 'resume', 'y27sbnykxcpdujp8', 'resume', 'headline', 'barton', 'miranda', 'barton', 'miranda', 'pmp', 'leechecker', 'court', 'sterling', 'virginia', 'bartmiranda', 'gmail', 'com', 'senior', 'level', 'project', 'manager', 'solutions', 'oriented', 'quality', 'focused', 'professional', 'offering', 'years', 'diverse', 'project', 'management', 'success', 'specializing', 'complex', 'technical', 'engagements', 'hands', 'resourceful', 'change', 'agent', 'providing', 'extensive', 'background', 'across', 'systems', 'integration', 'web', 'application', 'development', 'software', 'development', 'results', 'driven', 'team', 'leader', 'well', 'versed', 'updated', 'project', 'management', 'methodologies', 'forward', 'thinking', 'technologist', 'providing', 'record', 'improving', 'processes', 'procedures', 'optimizing', 'operations', 'controlling', 'costs', 'mitigating', 'risk', 'implementing', 'best', 'practices', 'delivering', 'complex', 'initiatives', 'within', 'strict', 'time', 'budget', 'quality', 'requirements', 'exhibit', 'valuable', 'combination', 'technical', 'business', 'communication', 'savvy', 'areas', 'expertise', 'project', 'program', 'management', 'strategic', 'planning', 'quality', 'assurance', 'process', 'improvement', 'cost', 'control', 'change', 'management', 'software', 'development', 'lifecycle', 'risk', 'mitigation', 'needs', 'assessment', 'budget', 'administration', 'documentation', 'systems', 'integration', 'communications', 'career', 'history', 'fannie', 'mae', 'washington', 'dc', 'present', 'government', 'sponsored', 'enterprise', 'operating', 'us', 'secondary', 'mortgage', 'market', 'senior', 'project', 'manager', 'provide', 'project', 'leadership', 'expertise', 'variety', 'initiatives', 'involving', 'systems', 'integration', 'website', 'implementation', 'applications', 'development', 'software', 'development', 'lifecycle', 'risk', 'mitigation', 'oversee', 'mentor', 'cross', 'functional', 'teams', 'ensure', 'compliance', 'time', 'budget', 'quality', 'requirements', 'key', 'engagements', 'include', 'asset', 'management', 'program', 'itam', 'present', 'program', 'manager', 'directed', 'overall', 'implementation', 'itam', 'solution', 'including', 'establishing', 'processes', 'technology', 'staff', 'charged', 'maintaining', 'physical', 'financial', 'operational', 'information', 'control', 'hardware', 'software', 'assets', 'performed', 'project', 'program', 'level', 'planning', 'tracking', 'reporting', 'business', 'technology', 'work', 'streams', 'made', 'program', 'controlled', '85m', 'budget', 'guided', 'managers', 'project', 'teams', 'meet', 'project', 'targets', 'implementation', 'itam', 'tools', 'processes', 'developed', 'executed', 'communications', 'plan', 'liaised', 'project', 'quality', 'office', 'pqo', 'ensure', 'software', 'development', 'lifecycle', 'deliverables', 'met', 'quality', 'standards', 'liaised', 'central', 'program', 'office', 'cpo', 'reduced', 'potential', 'program', 'risk', 'implementing', 'risk', 'management', 'plan', 'detailing', 'escalation', 'process', 'risks', 'identified', 'project', 'work', 'stream', 'level', 'successfully', 'transitioned', 'program', 'management', 'role', 'part', 'corporate', 'reorganization', 'included', 'assessing', 'existing', 'management', 'plans', 'schedules', 'implemented', 'changes', 'program', 'schedule', 'providing', 'realistic', 'expectations', 'stakeholders', 'mitigated', 'risks', 'overall', 'program', 'schedule', 'budget', 'scope', 'fannie', 'maeâ', 'rapid', 'transitioning', 'within', 'organization', 'including', 'release', 'departure', 'critical', 'itam', 'project', 'team', 'resources', 'development', 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'maxie', 'tranesia', 'yahoo', 'com', 'location', 'tx', 'start', 'resume', 'text', 'tranesia', 'maxie', 'murdeaux', 'ln', 'dallas', 'tx', 'us', 'maxie', 'tranesia', 'yahoo', 'com', 'mobile', 'home', 'phone', 'contact', 'preference', 'email', 'resume', 'monster', 'resume', 'pzxvqen27qh74ynt', 'resume', 'headline', 'tranesia', 'maxie', 'objective', 'customer', 'service', 'rep', 'seeking', 'entery', 'level', 'postion', 'willing', 'work', 'hard', 'bottom', 'help', 'build', 'customer', 'relations', 'resolving', 'complex', 'issues', 'use', 'tact', 'diplomacy', 'find', 'common', 'ground', 'achieve', 'win', 'win', 'come', 'listen', 'attentively', 'creatively', 'problems', 'solve', 'win', 'customers', 'loyalty', 'experience', 'valero', 'refining', 'company', 'inc', 'industry', 'retail', 'cashier', 'clerk', 'cleaning', 'bathrooms', 'gas', 'pumps', 'brake', 'hot', 'dog', 'grills', 'cleaning', 'instore', 'mopping', 'checking', 'customers', 'working', 'major', 'retail', 'vendors', 'cingular', 'wireless', 'services', 'inc', 'industry', 'telecommunications', 'services', 'customer', 'service', 'analyst', 'running', 'credit', 'check', 'review', 'customer', 'payment', 'history', 'add', 'new', 'service', 'adding', 'extra', 'lines', 'existing', 'service', 'taking', 'payments', 'billing', 'questions', 'directing', 'customer', 'calls', 'warm', 'transfers', 'working', 'majors', 'credit', 'companiesto', 'provent', 'fraud', 'education', 'h', 'grady', 'spurce', 'high', 'school', 'equivalent', 'cedar', 'valley', 'college', 'college', 'coursework', 'completed', 'certification', 'manor', 'care', 'certified', 'nurse', 'assistant', 'manor', 'care', 'cardio', 'pulmonary', 'resucitation', 'certification', 'skills', 'skill', 'name', 'skill', 'level', 'cleaning', 'typing', 'customer', 'service', 'ms', 'office', 'excel', 'windows', 'faxes', 'emails', 'expert', 'languages', 'language', 'proficiency', 'level', 'english', 'fluent', 'summary', 'current', 'career', 'level', 'experienced', 'non', 'manager', 'years', 'relevant', 'work', 'experience', 'years', 'work', 'status', 'us', 'authorized', 'work', 'country', 'employer', 'active', 'security', 'clearance', 'citizenship', 'none', 'target', 'job', 'target', 'job', 'title', 'customer', 'service', 'associate', 'target', 'locations', 'selected', 'locations', 'us', 'tx', 'dallas', 'relocate', 'willingness', 'travel', 'travel', 'basic', 'profile', 'resume', 'posted', 'target', 'job', 'titles', 'customer', 'service', 'associate', 'candidate', 'name', 'tranesia', 'maxie', 'location', 'murdeaux', 'ln', 'dallas', 'tx', 'phone', 'email', 'maxie', 'tranesia', 'yahoo', 'com', 'highest', 'degree', 'high', 'school', 'equivalent', 'recent', 'salary', 'employer', 'history', 'title', 'dates', 'cingular', 'wireless', 'services', 'inc', 'customer', 'service', 'analyst', 'valero', 'refining', 'company', 'inc', 'cashier', 'clerk', 'end', 'resume', 'text']]
In [224]:
len(query_doc)
Out[224]:
335
In [225]:
query_doc_bow = []
for doc in query_doc:
    query_doc_bow.append(dictio.doc2bow(doc))
In [226]:
len(query_doc_bow)
Out[226]:
335
In [227]:
query_doc_tf_idf = []
for doc in query_doc_bow:
    query_doc_tf_idf.append(tf_idf[query_doc_bow])
In [229]:
len(query_doc_tf_idf)
Out[229]:
335
In [230]:
query_doc_bow
Out[230]:
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In [231]:
similarity = []
for doc in query_doc_tf_idf:
    similarity.append(sims[doc])
In [235]:
len(similarity)
Out[235]:
335

similarity Array

In [234]:
similarity[0][0]
Out[234]:
array([ 0.35054645,  0.02322643,  0.08233488,  0.02395826,  0.00619038,
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        0.026081  ,  0.00672384,  0.00652127], dtype=float32)